DATA DRIVEN DONOR MANAGEMENT: LEVERAGING RECENCY & FREQUENCY IN DONATION BEHAVIOR
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1 DATA DRIVEN DONOR MANAGEMENT: LEVERAGING RECENCY & FREQUENCY IN DONATION BEHAVIOR Peer Fader Frances and Pei Yuan Chia Professor of Markeing Co Direcor, Wharon Cusomer Analyics Iniiaive The Wharon School of he Universiy of Pennsylvania hp://wcai.wharon.upenn.edu PAGE 1 HOW CAN YOU TELL WHICH DONORS ARE LAPSED AND WHICH ARE JUST DORMANT? Organizaion: A public radio saion suppored primarily by conribuions from is liseners Challenge: Looking a liseners hisories of wheher or no hey gave each year, wha can we predic abou heir fuure giving paerns? Focal donors: Iniial focus on 1995 cohor, ignoring donaion amoun 11,104 firs ime supporers who made a oal of 24,615 repea donaions over he nex 6 years Reference: Fader, Peer S., Bruce G.S. Hardie, and Jen Shang (2010), Cusomer Base Analysis in a Discree Time Nonconracual Seing, Markeing Science, 29 (6), PAGE 2 1
2 HOW MUCH WILL DONORS GIVE IN THE FUTURE? HOW DOES IT DEPEND ON THEIR PAST PATTERNS? ????? ????? ????? ????? ????? ????? ????? ????? ????? ????? ????? ????? ????? PAGE 3 LET S FIRST LOOK AT BOB : WHAT CAN WE PREDICT ABOUT HIS GIVING IN ? ????? ????? ????? ????? ????? ????? ????? BOB ????? ????? ?????... PAGE 4 2
3 WHAT CAN WE TELL ABOUT SARAH? SARAH ????? ????? ????? ????? ????? ????? ????? ????? ????? ????? ????? PAGE 5 HOW DO MARY AND SHARMILA COMPARE? SARAH ????? ????? ????? MARY ????? ????? ????? ????? BOB ????? SHARMILA ????? ?????... PAGE 6 3
4 WHAT DONATION BEHAVIOR CHARACTERISTICS DO WE NEED TO TAKE INTO ACCOUNT? GIVING BEHAVIORS DONOR TYPES RECENCY: How recenly did he donor give? When was he las ime he donor gave? FREQUENCY: How many imes did he donor give in he pas 6 years? Mos meaningful donaion paerns can be described by hese wo merics alone. ALIVE: DORMANT: LAPSED: Clearly an acive donor: giving frequenly and gave recenly Has no given recenly, bu is likely o give again wih he righ developmen promps Has no given recenly and is no likely o give again MARY ????? SHARMILA ????? PAGE 7 MORE ABOUT RECENCY AND FREQUENCY Y Y N N N N Y Y N N Y Y Wha does i mean when here s one or more no donaion a he end of a sequence? a) The donor lapsed (i.e., lef he donor pool) b) The donor is dorman (i.e., decided no o give ha year, didn hink of giving, ec.) c) We don know, bu can build a model o come up wih a bes guess Answer: c) We never know for sure wheher he donor is lapsed or no; based on recency and frequency of his donaion, we can make an educaed guess abou he probabiliy of lapsing, so we can decide where o devoe resources Based on our bes guesses abou he probabiliy of deah and propensiy o donae, we can calculae expeced frequency of fuure donaions for each donor PAGE 8 4
5 WHAT ABOUT MARY VERSUS CHRIS? ????? ????? ????? ????? MARY ????? ????? ????? ????? ????? CHRIS ????? ????? PAGE 9 YOU CAN TRY THIS AT HOME PAGE 10 5
6 BUY TILL YOU DIE MODEL We employ a Buy Till You Die model o predic fuure donaion behaviors The model only uses hree inpus: 1. Recency (R) 2. Frequency (F) 3. Number of people for each combinaion of R/F This requires a small amoun of daa and provides an easier srucure o work wih (i.e., daa are aggregaed from individual level o R/F groups) By assuming cerain probabiliy disribuions for donors propensiies, we can consruc a robus model ha is easy o implemen on Excel This BTYD modeling approach has a long rack record of success in a variey of differen domains PAGE 11 EXCEL IMPLEMENTATION Deailed sep-by-sep insrucions available a hp://brucehardie.com/noes/010/ PAGE 12 6
7 EXPECTED # OF DONATIONS IN AS A FUNCTION OF RECENCY AND FREQUENCY Name R F BOB SARAH MARY SHARMILA CHRIS # Rp Trans Year of Las Transacion ( ) PAGE 13 ANALYSIS Bob (R:6, F:6) is expeced o donae 3.75 imes ou of 5 opporuniies beween 2002 and 2006, surprisingly low given his 100% donaion rae Mary and Chris have he same RF (6,4), so heir expeced number of donaions going forward is he same Even hough Mary and Chris have lower F han Sharmila, heir higher R suggess ha hey are Alive, hus hey are 50% more valuable han Sharmila Sharmila (5,5), despie high donaion rae, has likely lapsed Sarah, wih very low R and F, is lapsed and/or a very ligh donor (hard o ell) # Rp Trans Year of Las Transacion ( ) PAGE 14 7
8 HOW DO WE KNOW THE MODEL WORKS? The model does a solid job of making predicions abou fuure donaion behaviors, as condiional expecaions correspond very well wih acual holdou period daa ( ) PAGE 15 HOW DO WE KNOW THE MODEL WORKS? Overall, he model is excepional good a fiing he hisorical daa as well as he holdou period daa (i.e., model predicions vs. acual number of donaions beween 2002 and 2006) PAGE 16 8
9 A SECOND ILLUSTRATION Using a larger daase from a differen non profi firm, we creae a hea map ha shows which combinaions of RF will likely yield he mos valuable donors x / x PAGE 17 PROBABILITY OF BEING ALIVE x/x PAGE 18 9
10 SUMMARY: HOW IS THIS METHOD DIFFERENT? There are many models ha predic fuure donaion behaviors; we believe our mehod is differen / superior because: 1. The model requires a very small amoun of daa (Recency and Frequency), compared o oher models ha require a large daase (ypically deailed individual level characerisics, e.g., demographics) 2. The model has demonsraed robus ou of sample validaion 3. The model can be generalized o oher ypes of behaviors; i is no excessively cusomized o he donaion domain 4. The model can easily be implemened on Excel; i does no require any proprieary or specialized sofware PAGE 19 WANT MORE? For large scale daabases, use he new open source BTYD R Library: hp://cran.r projec.org/web/packages/btyd/btyd.pdf While his model offers accurae predicions and useful insighs abou how o undersand donaion propensiies, i sops shor of offering any specific advice abou which donors o arge and when / how o do so Building on his model, Schweidel and Knox explore he impac of fundraising effors on donaion aciviy in a new paper: Incorporaing Direc Markeing Aciviy ino Laen Ariion Models (hp://ssrn.com/absrac= ) PAGE 20 10
11 DISCUSSION Professor Peer Fader Wharon Cusomer Analyics Iniiaive PAGE 21 11
Peter Fader Professor of Marketing, The Wharton School Co-Director, Wharton Customer Analytics Initiative
DATA-DRIVEN DONOR MANAGEMENT Peter Fader Professor of Marketing, The Wharton School Co-Director, Wharton Customer Analytics Initiative David Schweidel Assistant Professor of Marketing, University of Wisconsin-
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